Development of hemodiagnosis for early detection of ovarian cancer with AI using glycopeptide peaks obtained from CSGSA (Comprehensive Serum Glycopeptide Spectra Analysis)
Project/Area Number |
18K09300
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 56040:Obstetrics and gynecology-related
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Research Institution | Tokai University |
Principal Investigator |
IKEDA Masae 東海大学, 医学部, 講師 (20365993)
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Co-Investigator(Kenkyū-buntansha) |
信田 政子 東海大学, 医学部, 講師 (10338717)
三上 幹男 東海大学, 医学部, 教授 (30190606)
柴田 健雄 東海大学, 健康学部, 講師 (30366033)
平澤 猛 東海大学, 医学部, 准教授 (70307289)
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Project Period (FY) |
2018-04-01 – 2021-03-31
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Project Status |
Completed (Fiscal Year 2020)
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Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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Keywords | 卵巣癌 / 血清バイオマーカー / 糖ペプチド / 質量分析 / 人工知能 / 深層学習 / リキッドバイオプシー / 腫瘍マーカー / 早期診断 / がん検診 |
Outline of Final Research Achievements |
Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based CSGSA method (CSGSA-AI) in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.
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Academic Significance and Societal Importance of the Research Achievements |
卵巣癌は早期発見が難しくかつ予後も極めて悪い癌であり、新たな発想の新規診断技術導入が重要である。腫瘍マーカーは単一分子と認識され研究されてきたが、現状では卵巣癌早期診断は不可能であろう。そこで古い概念を打ち破り、究極のCombination Assayと考えられる網羅的血清糖ペプチドピークと人工知能を用いた卵巣癌早期診断の開発し、現在汎用されている卵巣癌マーカーであるCA125とHE4よりも有意に初期卵巣癌を判別できる診断法を開発した。
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Report
(4 results)
Research Products
(18 results)
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[Journal Article] Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer.2020
Author(s)
Tanabe K, Ikeda M, Hayashi M, Matsuo K, Yasaka M, Machida H, Shida M, Katahira T, Imanishi T, Hirasawa T, Sato K, Yoshida H, Mikami M.
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Journal Title
Cancers (Basel)
Volume: 12(9)
Issue: 9
Pages: 2373-2373
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] Epidemiological guideline influence on the therapeutic trend and patient outcome of uterine cervical cancer in Japan: Japan society of gynecologic oncology guideline evaluation committee project.2020
Author(s)
Shigeta S, Shida M, Nagase S, Ikeda M, Takahashi F, Shibata T, Yamagami W, Katabuchi H, Yaegashi N, Aoki D, Mikami M
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Journal Title
Gynecol Oncol
Volume: 159
Issue: 1
Pages: 248-255
DOI
Related Report
Peer Reviewed / Open Access
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[Journal Article] Utility of Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA) for the Detection of Early Stage Epithelial Ovarian Cancer.2020
Author(s)
Matsuo K, Tanabe K, Hayashi M, Ikeda M, Yasaka M, Machida H, Shida M, Sato K, Yoshida H, Hirasawa T, Imanishi T, Mikami M.
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Journal Title
Cancers (Basel)
Volume: 12(9)
Issue: 9
Pages: 2374-2374
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer.2019
Author(s)
Hayashi M, Matsuo K, Tanabe K, Ikeda M, Miyazawa M, Yasaka M, Machida H, Shida M, Imanishi T, Grubbs BH, Hirasawa T, Mikami M.
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Journal Title
Cancers (Basel)
Volume: 11(5)
Issue: 5
Pages: 591-591
DOI
NAID
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Presentation] NGS-based molecular profiling ( a multi-center collaborative, observation study in Japan) highlights pathogenic variants of DNA-repair genes in advanced or recurrent endometrial cancer.2020
Author(s)
Fujiwara H, Oda K, Takahashi N, Sakata J, Taneichi A, Ikeda M, Tanikawa M, Kusakabe M, Mitsuhashi A, Kobayashi Y, Yamashita H, Suzuki N, Akiyama A, Tokunaga H, Tanaka N, Mikami M
Organizer
ASCO2020 The American Society of Clinical Oncology
Related Report
Int'l Joint Research
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